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http://metagenomics.iiserb.ac.in/mp3/

Software tool for prediction of pathogenic proteins in genomic and metagenomic data. Used for identification of partial pathogenic proteins predicted from short (100-150 bp) metagenomic reads and also performs on complete protein sequences., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: MP3 tool (RRID:SCR_019282) Copy   

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https://www.robotreviewer.net/about

Open source web based system that uses machine learning and NLP to semi automate biomedical evidence synthesis, to aid practice of Evidence Based Medicine. Processes full text journal articles describing randomized controlled trials. Designed to automatically extract key data items from reports of clinical trials.

Proper citation: RobotReviewer (RRID:SCR_021064) Copy   

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https://github.com/BackofenLab/RNAProt

Software tool for modelling RNA binding protein binding preferences. Used to predict RPB binding sites. Computational RBP binding site prediction framework based on recurrent neural networks. Includes functionalities from dataset generation over model training to evaluation of binding preferences and binding site prediction.

Proper citation: RNAProt (RRID:SCR_021218) Copy   

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https://doi.org/10.7910/DVN/XD1A6B

Portal for cross sectional study where population based sample of adult cancer patients were recruited for mailed survey (with telephone follow-up of non-responders) to evaluate equivalence of PROMIS measures across socio-demographic and clinical sub-groups.

Proper citation: PROMIS 2 MY Health (RRID:SCR_021215) Copy   

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https://github.com/HeardLibrary/linked-data/tree/master/vanderbot

Software Python tool as Vanderbilt University curation tool.Used for exporting public profiles of university faculty from Vanderbilt University database into Wikidata.Used to create or update items in Wikidata using data from CSV spreadsheet data.

Proper citation: VanderBot (RRID:SCR_021298) Copy   

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https://emea.support.illumina.com/sequencing/sequencing_software/experiment_manager/downloads.html

Software to create and edit sample sheet in order to perform next generation sequencing on Illumina paltforms. Used for MiniSeq, MiSeq, NextSeq 500/550, HiSeq, and NovaSeq 6000 Systems.

Proper citation: Illumina Experiment Manager (RRID:SCR_021202) Copy   

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https://gradepro.org/

Software tool used to create summary of findings tables for cochrane systematic reviews. Web application to create, manage and share summaries of research evidence called Evidence Profiles and Summary of Findings Tables.

Proper citation: GRADEpro (RRID:SCR_021308) Copy   

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http://wpicr.wpic.pitt.edu/WPICCompGen/genomic_control/genomic_control.htm

Software application where GC implements the genomic control models. GCF implements the basic Genomic Control approach, but adjusts the p-values for uncertainty in the estimated effect of substructure. This approach is preferable if a large number of tests will be evaluated because it provides a more accurrate assessment of the significance level for small p-values. (entry from Genetic Analysis Software)

Proper citation: GC/GCF (RRID:SCR_009075) Copy   

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http://wpicr.wpic.pitt.edu/WPICCompGen/newcovibd/covibd.htm

Software application that refines linkage analysis of affected sibpairs by considering attributes or environmental exposures thought to affect disease liability. This refinement utilizes a mixture model in which a disease mutation segregates in only a fraction of the sibships, with the rest of the sibships unlinked. Covariate information is used to predict membership within the two groups corresponding to the linked and unlinked sibships. The pre-clustering model uses covariate information to first form two probabilistic clusters and then tests for excess IBD-sharing in the clusters. The Cov-IBD model determines probabilistic group membership by joint consideration of covariate and IBD values. (entry from Genetic Analysis Software)

Proper citation: COVIBD (RRID:SCR_009155) Copy   

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http://aws.amazon.com/1000genomes/

A dataset containing the full genomic sequence of 1,700 individuals, freely available for research use. The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year. Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data. All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP. Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.

Proper citation: 1000 Genomes Project and AWS (RRID:SCR_008801) Copy   

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http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/04432#summary

Data set from a long-term population-based prospective study of non-institutionalized residents (aged 21 or older, or aged 16-21 and older if married) in Alameda County, California investigating social and behavioral risk factors for morbidity, mortality, functioning and health. Questions were asked on marital and life satisfaction, parenting, physical activities, employment, health status, and childhood experiences. Demographic information on age, race, height, weight, education, income, and religion was also collected. Included with this dataset is a separate file (part 2) containing mortality data. With the aging of this cohort, data are becoming increasingly valuable for examining the life-long cumulative effects of social and behavioral factors on a well-characterized population. The first wave collected information for 6,928 respondents (including approximately 500 women aged 65 years and older) on chronic health conditions, health behaviors, social involvements, and psychological characteristics. The 1974 questionnaire was sent to 6,246 living subjects who had responded in 1965, and were able to be located. The third wave provides a follow-up of 2,729 original 1965 and 1974 respondents and examines health behaviors such as alcohol consumption and smoking habits, along with social activities. Also included is information on health conditions such as diabetes, osteoporosis, hormone replacement, and mental illness. Another central topic investigated is activities of daily living (including self-care such as dressing, eating, and shopping), along with use of free time and level of involvement in social, recreational, religious, and environmental groups. The fourth wave is a follow-up to the 1994 panel and examines changes in functional abilities such as self-care activities, employment, involvement in community activities, visiting friends/family, and use of free time since 1994. * Dates of Study: 1965-1999 * Sample Size: 1965: 6,928; 1974: 4,864; 1994: 2,729; 1995: 2,569, 1999: 2,123 * Study Features: Longitudinal Links: * 1965 ICPSR, http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06688 * 1974 ICPSR, http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06838 * 1994 and 1995 ICPSR, http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03083 * 1999 ICPSR, http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/04432#summary

Proper citation: Alameda County Health and Ways of Living Study (RRID:SCR_008889) Copy   

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http://www.nber.org/papers/h0038

A dataset to advance the study of life-cycle interactions of biomedical and socioeconomic factors in the aging process. The EI project has assembled a variety of large datasets covering the life histories of approximately 39,616 white male volunteers (drawn from a random sample of 331 companies) who served in the Union Army (UA), and of about 6,000 African-American veterans from 51 randomly selected United States Colored Troops companies (USCT). Their military records were linked to pension and medical records that detailed the soldiers������?? health status and socioeconomic and family characteristics. Each soldier was searched for in the US decennial census for the years in which they were most likely to be found alive (1850, 1860, 1880, 1900, 1910). In addition, a sample consisting of 70,000 men examined for service in the Union Army between September 1864 and April 1865 has been assembled and linked only to census records. These records will be useful for life-cycle comparisons of those accepted and rejected for service. Military Data: The military service and wartime medical histories of the UA and USCT men were collected from the Union Army and United States Colored Troops military service records, carded medical records, and other wartime documents. Pension Data: Wherever possible, the UA and USCT samples have been linked to pension records, including surgeon''''s certificates. About 70% of men in the Union Army sample have a pension. These records provide the bulk of the socioeconomic and demographic information on these men from the late 1800s through the early 1900s, including family structure and employment information. In addition, the surgeon''''s certificates provide rich medical histories, with an average of 5 examinations per linked recruit for the UA, and about 2.5 exams per USCT recruit. Census Data: Both early and late-age familial and socioeconomic information is collected from the manuscript schedules of the federal censuses of 1850, 1860, 1870 (incomplete), 1880, 1900, and 1910. Data Availability: All of the datasets (Military Union Army; linked Census; Surgeon''''s Certificates; Examination Records, and supporting ecological and environmental variables) are publicly available from ICPSR. In addition, copies on CD-ROM may be obtained from the CPE, which also maintains an interactive Internet Data Archive and Documentation Library, which can be accessed on the Project Website. * Dates of Study: 1850-1910 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** Union Army: 35,747 ** Colored Troops: 6,187 ** Examination Sample: 70,800 ICPSR Link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06836

Proper citation: Early Indicators of Later Work Levels Disease and Death (EI) - Union Army Samples Public Health and Ecological Datasets (RRID:SCR_008921) Copy   

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http://www.icpsr.umich.edu/icpsrweb/NACDA/studies/02744/version/1

Data set of a follow-up study (one of four Established Populations for Epidemiologic Studies of the Elderly - EPESE) that obtains information on four primary outcome variables (cognitive status, depression, functional status, and mortality) and four primary independent variables (social support, social class, social location, and chronic illness); and examines the relationships between social factors and chronic disease on the one hand and health outcomes on the other. This data set complements the other three sites providing a population which is both urban and rural and contains approximately equal numbers of black and white participants across a broad socioeconomic base. The Duke site was originally funded by the NIA Epidemiology, Demography and Biometry Program (EDBP) to complete seven waves of data collection (three in-person and four telephone interviews) in order to examine the health of a sample of 4,162 persons aged 65+, and factors that influence their health and use of health services. The cohort was originally interviewed in 1986/87 and followed annually for 6 years thereafter. The study design consisted of a random stratified household sample with an over-sampling of blacks. Questionnaire topics include the following: Demographics, Alcohol Use, Independence, Health condition, Cognition, Personal mastery, Health Service Utilization, Activity of daily living, Social Support, Hearing and Vision, Incontinence, Social Interaction, Weight and Height, Smoking, Religion, Nutrition, Life Satisfaction, Self Esteem, Sleep, Medications, Economic Status, Depression, Life Changes, Blood pressure. National Death Index files have been searched and death certificates obtained for the members of this study. Sample members have been matched with Medicare Part A files to obtain information on hospitalizations, and will be matched on Medicare Part B (outpatient) files. Data from the first wave of the survey is in the public domain and can be obtained from NACDA or from the National Archives, Center for Electronic Records in Washington, DC. * Dates of Study: 1996-1997 * Study Features: Longitudinal, Oversampling * Sample Size: 1986-1988: 4,162 Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02744 * National Archives: http://www.archives.gov/research/electronic-records/

Proper citation: Piedmont Health Survey of the Elderly (RRID:SCR_006349) Copy   

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http://www.sanger.ac.uk/Projects/Fungi/

Fungal genomes available from the Sanger Institute. Data are accessible in a number of ways; for each organism there is a BLAST server, allowing search of the sequences. Sequences can also be down-loaded directly by FTP. In addition, for those organisms being sequenced using a cosmid approach, finished and annotated cosmids are submitted to EMBL and other public databases.

Proper citation: Fungi Sequencing Projects (RRID:SCR_008524) Copy   

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http://degradome.uniovi.es/domains.html

Domains found in human and mouse proteases colour-coded according to the catalytic class in which they appear. Some of them appear in more than one catalytic group, and two-colours are used. Yellow, aspartyl proteases; blue, cysteine proteases; green, metalloproteases; and red, serine proteases.

Proper citation: Ancillary Domains Associated With Human and Mouse Proteases (RRID:SCR_008363) Copy   

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http://www.cs.tau.ac.il/~shlomito/tissue-net/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. Network visualizations in which the expression and predicted flux data are projected over the global human network. These network visualizations are accessible through the supplemental website using the publicly available Cytoscape software (Cline, Smoot et al. 2007). Since many high degree nodes exist in the network, special layouts are required to produce network visualizations that are readily interpretable. To this end we produced network visualizations in which hub nodes are repeated multiple times and hence layouts with a small number of edge crossings can be generated. Contains entries for brain compartments and brain pathways.

Proper citation: Network-based Prediction of Human Tissue-specific Metabolism (RRID:SCR_007392) Copy   

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http://www.unisi.it/internet/home.html

Proper citation: University of Siena; Tuscany; Italy (RRID:SCR_008080) Copy   

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http://www.unipi.it/english/

Proper citation: University of Pisa; Pisa; Italy (RRID:SCR_006616) Copy   

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http://mialab.mrn.org/data/index.html

An MRI data set that demonstrates the utility of a mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12-71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described, provide a useful baseline for future investigations of brain networks in health and disease.

Proper citation: MIALAB - Resting State Data (RRID:SCR_008914) Copy   

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http://prehco.rcm.upr.edu/

A dataset that provides researchers and policy makers information about issues affecting the elderly population in Puerto Rico: health status, housing arrangements, functional status, transfers, labor history, migration, income, childhood characteristics, health insurance, use of health services, marital history, mistreat, sexuality, etc. It investigates the characteristics of older adults (aged 60+) through an island-wide cross-sectional sample survey of target individuals and their surviving spouses. The sampling frame was constructed on the basis of an advance release of the 2000 US Census. The population for the study consists of the elderly population (60+) in households in Puerto Rico. The sample design used a multistage probabilistic sample by cluster. All elderly adults who lived in the selected households were eligible. If more than one person was in the target population, one 60+ adult was the target and one was the spouse. Respondents 80+ and males in couples who were both 80+ were oversampled. There were 4,293 targets aged 60+ and 1,444 spouses (all ages) in the first wave. Types of data include demographic; household composition; marital history; Cantrill Scale; mini-mental (designed to measure cognitive capacity of Spanish-speaking Latinos with low levels of education and to provide early indications of dementia); self-reported health status; diagnosed health conditions; childhood conditions; transfers; labor history; migration; housing; assets; Activities of Daily Living; Instrumental Activities of Daily Living; medicines; health insurance and use of health services; family structure; sexuality; anthropometric measures. Project innovations include: (1) the design and test of a new tool for assessing cognition among Spanish speaking elderly of low levels of education, (2) a symptoms section to assess the validity of selected self reported conditions, (3) a modification of the Cantrill''s Ladder Scale, (4) protocols for physical measurements to assess current, as well as past, conditions, and (5) the use of GIS and GPS in the fieldwork supervision and to geocoding the survey data. At this moment PREHCO has completed a second wave to become a longitudinal study. The questionnaire included questions regarding the changing conditions (health, residential, social and economic) of those individuals who responded the first questionnaire. The new questionnaire included novel components: vignettes for health status self-report, a new improved section on disability and dependency, and on labor force participation. We also expanded the section of anthropometry by adding a few measurements and physical efficiency tests. Those participants deceased or institutionalized were interviewed using a proxy. Data Availability: First and second wave data are available for public use through BADGIR, the online data archive at the University of Wisconsin-Madison, at: http://nesstar.ssc.wisc.edu/ * Dates of Study: 2002-2003, 2004-2006 * Study Features: Longitudinal, International, Minority Oversampling, Anthropometric measures * Sample Size: 5,336

Proper citation: Puerto Rican Elderly: Health Conditions (RRID:SCR_008916) Copy   

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